SLAM via Variable Reduction from Constraint Maps

Abstract

The two dominant forms of SLAM are based on Extended Kalman Filtering and Consistent Pose Estimation. We show that these are particular subsets of a more general view of the SLAM problem, in which variables representing all robot poses and features are kept. The general technique of variable reduction is a unifying view of these methods that is mathematically sound, and which enables us to explore other interesting and computationally compelling forms for solving SLAM problems.